Abstract
Support vector machines (SVMs) are one of the most powerful learning algorithms for solving classification problems. However, in their original formulation, they only deal with binary classification. Traditional extensions of the binary SVMs for multiclass problems are based either on decomposing the problem into a number of binary classification problems, which are then independently solved, or on reformulating the objective function by solving larger optimization problems. In this paper, we propose (MCESVM)-E-2, an approach for multiclass classification based on the cooperative evolution of SVMs. Cooperative evolution allows us to decompose an M-class problem into M subproblems, which are simultaneously optimized in a cooperative fashion. We have reformulated the optimization problem such that it focuses on learning the support vectors for each class at the time that it takes into account the information from other classes. A comprehensive experimental study using common benchmark datasets is carried out to validate MC2ESVM. The experimental results, supported by statistical tests, show the effectiveness of MC2ESVM for solving multiclass classification problems, while keeping a reasonable number of support vectors.